Development of an Algorithm to Denoise HFNO-generated Tracheal Sound

Endoscopy, Gastrointestinal Clinical Trial

Official title: Development of Algorithm to Precise Monitoring Respiratory Sound During Deep Sedation Using the High Flow Nasal Oxygen System

Status Recruiting

Clinical Trial Summary

This study aims to develop an algorithm to mitigate the noise generated by the high-flow nasal oxygen system for tracheal sound monitoring in deeply sedated patients.

Clinical Trial Description

During deep sedation without intubation, anesthesia respiratory care and monitoring are of utmost importance. Anesthesia can lead to adverse respiratory effects, increasing the risk of airway obstruction and respiratory depression. High-flow nasal oxygen (HFNO) systems are often used to mitigate these risks. Monitoring tracheal breathing sounds directly using amplification provides better detection accuracy. However, traditional auscultation instruments have limitations, including noise interference and lack of visual functionality. The Airmod Smart Respiratory Monitoring System, with FDA approval in the United States and Taiwan, addresses these issues. It offers respiratory sound recording, noise filtering, event logging, and respiratory rate analysis. Currently, no digital stethoscope on the market can filter out HFNO noise. Therefore, the current study is to develop an algorithm to mitigate the noise generated by the high-flow nasal oxygen system for tracheal sound monitoring in deeply sedated patients. ;

Related Conditions & MeSH terms

• Endoscopy, Gastrointestinal
• Sedation, Conscious

• NCT number: NCT06218017
• Study type: Interventional
• Source: National Taiwan University Hospital
• Contact: Chun-Yu Wu, MD
• Phone: 800-555-5555
• Email: b001089018@tmu.edu.tw
• Status: Recruiting
• Phase: N/A
• Start date: January 2024
• Completion date: January 2025